PayDash sped up India's workfare wages with a visibility dashboard

MGNREGA guarantees up to 100 days of paid manual labor a year to any rural Indian household that wants it, and its wage payments underpin the program's entire promise of reliable income during hard times — but payments frequently stretched well beyond the legally mandated processing window, sometimes by months. The default diagnosis, in press coverage and government responses alike, was corruption: some official somewhere in the long processing chain was skimming or deliberately slow-walking a payment for a bribe.

Researchers Eric Dodge, Yusuf Neggers, Rohini Pande and Charity Troyer Moore, working with India's Ministry of Rural Development, tested that diagnosis directly rather than assuming it. They built PayDash, a mobile and web dashboard that reorganized information the government's own management system already collected — showing, in real time, exactly which payments were stuck, how long they'd been stuck, and which specific officer was responsible, with a one-tap way to follow up by phone or WhatsApp — and randomized which officials, across three Indian states, actually got access to it.

Gave officials a real-time visibility dashboard instead of more audits, isolating that delays came from lack of information, not dishonesty — and it worked.

Randomized access isolated information as the lever, not punishment.

Dashboard reorganized existing data, so it was cheap and scalable.

Effect held for frontline managers alone, showing bottleneck was visibility.

Reduced punitive transfers, as real info made blame less necessary.

Before punishing for delays, test if the real constraint is lack of visibility—give responsible people real-time info and see if that alone fixes it.

PayDash access sped up bureaucrat processing of workfare payments by 17%, and increased available worksites and participating household work-days by 23% and 10% respectively, with the largest gains concentrated in the agricultural lean season — exactly when the safety net matters most.